Digital Commons Network^™

A Hybrid Deep Learning Model Combining Cnn And Extreme Learning Machine For Cyberattack Classification, Israa S. Kamil

Journal of Intelligent Informatics, Networking, and Cybersecurity

As ransomware attacks and zero-day exploits grow sophisticated, the need for intelligent, accurate systems to detect such threats becomes clearer. In this paper, a hybrid learning model based on Convolutional Neural Networks (CNNs) and Extreme Learning Machine (ELM) is presented to improve multiclass classification performance for cybersecurity applications. The framework combines CNNs' hierarchical feature learning with ELMs' fast classification. An attention mechanism that assigns weights to each feature based on importance is included in the final model. The hybrid model performed well on the metrics: precision = 0.97, recall = 0.98, and F1- score = 0.97, and, as expected from …

Towards Intelligent Iot-Ndn Security: Ai-Driven Pit Attack Detection And Cache Attack Analysis, Sura Haidar Ali, Alaa Shawqi Jaber

Journal of Intelligent Informatics, Networking, and Cybersecurity

Beginning with Named Data Networking (NDN), an early form of information-centric networks, the paradigm of how data is transmitted over a network was changed through the use of ``content-based'' communication instead of ``host-based'', while creating native caching at intermediate points along the path to each destination, and improving upon the security of all previous paradigms. NDN contains many inherent benefits such as caching, security, etc., but like any other paradigm, NDN creates new types of vulnerabilities, particularly within some of the key elements of this paradigm; namely the Content Store (CS), Pending Interest Table (PIT), and the Forwarding Information Base …

Escaping The Cyberstorm: A Gamified Social Engineering Training Program, Noah Mcclanahan, Fadi Abu-Amara, Ali Khattab, Travis Jett, Andre Jackson

Journal of Cybersecurity Education, Research and Practice

In this research work, we explored the effectiveness of gamification in improving cybersecurity awareness and training users on targeted social engineering attacks. Traditional cybersecurity training focuses on lectures and videos. These training methods may not actively engage employees, which reduces their knowledge retention and ability to recognize social engineering attacks. This lack of involvement is a concern, as social engineering continues to be one of the most prevalent attack methods faced by end-users. A gamified training program, Escaping the Cyberstorm, was developed using the Godot game engine to address key challenges in spreading cybersecurity awareness. The game includes real-life …

Stop Blaming My Users: Illumination Of The Technocentric Mythos Bias, Ervin H. Frenzel, Richard Lightcap

Journal of Cybersecurity Education, Research and Practice

 Abstract -This conceptual essay addresses the need for systemic and systematic transdisciplinary analytical techniques within cybersecurity and technical security. This conceptual essay is contingent upon recognition that cybersecurity is not simply technical in nature, it does not need an adversary, and more importantly it is based upon systems engineering and systems thinking.  The essay contributes a socio-technical attribution chain and field-specific ontology/taxonomy which distinguish user-triggered events from root causes, latent conditions, technical debt, validation failures, governance failures, and attribution bias before assigning responsibility to end users. It systematically defines an ontology inclusive of developer technical debt, organizational debt arising from …

Artificial Intelligence Mechanisms In The Limit Of Crimes And Law Enforcement, Saad Mefleh Alsuwaileh

Journal of Police and Legal Sciences

This study explores the potential of employing technological mechanisms and modern innovations brought about by the Fourth Industrial Revolution, particularly advancements in the field of information technology, in the domains of criminal investigation, crime prevention, and law enforcement. It aims to analyze the impact of these technologies on crime control efforts and the promotion of justice.

The significance of the study lies in highlighting the power of technology in processing and analyzing massive volumes of data with greater speed and accuracy, thereby enhancing the efficiency of criminal investigations and the ability to predict and prevent crimes. The core research question …

Match Made In Ml: Developing Compatibility Relationships In Evidential Reasoning Approaches With Machine Learning, Ella Jolie Thomas

Master's Theses

The presented expectation maximization informed evidential reasoning model extends the ability of the evidential reasoning calculus to support decision making by integrating an adaptive model learning capability. Compatibility relationships in Evidential Reasoning models are traditionally built by human domain experts. This process is labor-intensive, especially for large and complex models. Additionally, when new data becomes available, compatibility relationships must be reconstructed. Using machine learning and the expectation maximization algorithm, it is demonstrated that compatibility relationships can be constructed that learn relationships between domain knowledge that is used to make decisions. Using drug development as a domain of application, a traditional …

Can Machines Testify? Llms And The Boundaries Of Testimonial Epistemology, Michael J. Cummins

Philosophy Summer Fellows

As Large Language Models and AI chatbots become increasingly prevalent, pressing questions are raised about whether beliefs formed through LLM interactions carry the same epistemic weight as beliefs formed through human testimony. How we answer this question depends on whether LLMs can function as testifiers, a role which is typically assumed to require a human or human-like agent. This assumption has gone largely unexamined, yet its consequences are significant: if LLM outputs cannot constitute testimony, then the justificatory tools of testimonial epistemology are unavailable to any beliefs formed through LLM interaction. This paper challenges that assumption. It first argues that …

Stylometric And Formal Patterns In The Scholarly Impact Of Scientific Literature, Joshua Ange, Eric Godat, Rajani Sudan

SMU Journal of Undergraduate Research

Scientific communication is typically tied to promoting public engagement and interest in science, increasing scientific literacy, and playing an essential role in policymaking. The success of public communication of scientific findings is largely associated with secondary characteristics of research (e.g. the style of writing and presentation), rather than the primary content or research quality. But it is unclear to what extent the success of scientific literature intended for working scientists is influenced by those same secondary characteristics. Does the writing style of scientific articles impact their success in academic spheres? In this study, we explore the stylometric and formal characteristics …

Image Fusion Based On Deep Learning With Different Locations And Sizes Of Objects, Baneen Al-Kalabi, Tawfiq Al-Assadi

Journal of Intelligent Informatics, Networking, and Cybersecurity

Multi-focus image fusion combines partially focused images into a single all-in-focus composite. Existing object-based methods assume precise spatial and scale alignment across source images, an assumption that frequently fails in Misaligned Multi-Focus Dataset scenarios due to camera displacement and focal length variation. This paper proposes a novel training-free, object-aware fusion framework to address this limitation through a five-stage pipeline: YOLOv8x detection, SAM2-L segmentation, LoFTR correspondence matching, a novel Scale-Aware Area Resize Algorithm, and GLCM-guided MSB/LSB bit-level fusion. The framework was evaluated on the EDMF benchmark (20 image pairs, synthetically modified to simulate Misaligned Multi-Focus Dataset shifts) and a Misaligned Multi-Focus …

Lossless Medical Image Compression Using Integer Discrete Wavelet Transform With Adaptive Subband Differencing And Context-Adaptive Entropy Coding, Rasha F. Nadhim, Ibrahim Adel Ibrahim, Ashwaq T. Hashim

Journal of Intelligent Informatics, Networking, and Cybersecurity

From transform-domain decorrelation and adaptive entropy coding, we propose a method for efficient lossless compression of medical images in this work. We implement the Integer Discrete Wavelet Transform (IDWT) to decompose the input image into four subbands of LL, LH, HL, HH, encompassing approximation and directional detail elements, in the initial implementation. It also removes spatial redundancy in image information and decomposes image information into a less correlated and more sparsely distributed set of coefficients. To decrease redundancy further, it proposes a subband-dependent differencing scheme, which decorrelates neighbouring wavelet coefficients with directional prediction. So horizontal differencing is done on LH, …

A Blockchain-Integrated Federated Learning Model And Autoencoder-Based Feature Reduction For Improving Iot Intrusion Detection, Tahseen A. Wotaifi

Journal of Intelligent Informatics, Networking, and Cybersecurity

The rapid growth of Internet of Things (IoT) environments has brought forth a wealth of security challenges in detecting network intrusions in diverse and resource-restricted systems. Privacy, scalability, and single point of failure issues plague traditional centralized intrusion detection solutions. To address these challenges, the study proposes a secure and adaptive intrusion detection model using Federated Learning (FL) and Blockchain, augmented with autoencoder-based feature reduction. The ToN-IoT dataset is pre-processed, and then an unsupervised autoencoder is used to build informative low-dimensional feature representations. The processed data is deployed to various clients to mimic a real federated situation. Every client will …

A Dual- Task Hierarchical Graph Attention Network For Protein-Protein Interaction Sites Prediction, Oras A. Hussein, Eman S. Al-Shamery

Journal of Intelligent Informatics, Networking, and Cybersecurity

In computational structural biology, it is still very hard to accurately find protein-protein interaction (PPI) sites and estimate how strong the interaction would be. In this research, we provide an innovative two-stage deep learning framework that combines residue-level graph representation learning with protein-level regression to achieve a thorough modeling of protein interactions. Protein structures first encoded as residue graphs, with nodes that stand for amino acids and edges that show how close they are to each other in space. To find binding residues, a deep residual Graph Attention Network v2 (GATv2) uses multi-head attention, residual connections, and Jumping Knowledge aggregation …

The Climasentar Dataset For Evaluating Cross-Domain Transferability Of Large Language Models In Climate Sentiment Analysis, Sanaa Kaddoura, Mariam Alzaabi, Reem Nassar

All Works

Arabic research on climate sentiment analysis remains limited due to linguistic diversity, limited resources, and the scarcity of annotated datasets. Existing approaches primarily rely on questionnaires or general-purpose sentiment analysis models, which often fail to capture domain-specific sentiment expressions. To address this gap, this paper introduces ClimaSentAR , the first large-scale Arabic climate-focused sentiment dataset comprising 23,957 samples from the X platform. Native Arabic speakers annotate the dataset with a moderate inter-annotator agreement of 0.45. It contains 5510 positive, 8140 negative, and 10,307 neutral instances. To validate the importance and robustness of ClimaSentAR and address the gap in Arabic climate …

Cytotoxicity Of Bioactive Peptides From Cowpea (Vigna Unguiculata L. Walp) Against Breast Cancer Cells (Mcf-7), Dian R. Ningsih, Zusfahair Zusfahair, Ely Setiawan, Purwati Purwati, Kasta Gurning, Fitri A. Puspita Sari, Elya Widiawati

Karbala International Journal of Modern Science

Cancer is characterized by dysregulated metabolic signaling pathways, leading to uncontrolled cellular proliferation. Current cancer treatments, which involve surgery, radiotherapy, and chemotherapy, still cause adverse systemic toxicities. Accordingly, one possible effort that can be undertaken is to explore safe anticancer substances from protein sources such as peptides. Bioactive peptides can be isolated by hydrolyzing cowpea (Vigna unguiculata L. Walp) protein with trypsin. The objectives of this study were to isolate and fractionate bioactive peptides from cowpea, to determine the anticancer activity of peptide fractions, to identify active peptide fractions as anticancer agents using Liquid Chromatography High Resolution Mass Spectrometry …

Exogenous Ribosome Incorporation Induces Lineage Transdifferentiation In Intestinal And Liver Cancer Cells, Haoxuan Gu, Nadia Wahyuningsih, Keisuke Yamashita, Shota Inoue, Fuchao Tan, Mani Sharaki, Muhaimin Rifa’I, Kunimasa Ohta

Karbala International Journal of Modern Science

It has previously been shown that exogenous bacterial ribosomes added to somatic cells and various cancer cell lines generate ribosome-induced cell clusters (RICs) capable of transdifferentiating into multiple cellular lineages. However, the scope of ribosome-induced transdifferentiation in cancer cells remains poorly understood. This study aimed to analyze the effects of exogenous ribosome incorporation on human cancer cell lines, specifically the intestinal epithelial cell line Caco-2 and the hepatocellular carcinoma cell line HepG2. Caco-2 and HepG2 cells were cultured with purified bacterial ribosomes in human ES/iPS cell medium after trypsinization, resulting in the formation of ribosome-induced Caco-2 (RICs-Ca) and HepG2 (RICs-He) …

Mathematically Driven Enhancement In Information Security, Suhaib Badran, Asmaa Alqassab

Karbala International Journal of Modern Science

A new mathematically based cryptographic method has been proposed to improve information security; it involves transforming data into a matrix and performing bit-level operations. This decryption then follows the basic mechanism of a deterministic, fully invertible algorithm, with this invertible algorithm having the same principle as the existing ones: the process of decryption is symmetrical, where the process is reversed based on the input of the ciphertext. The plaintext is transformed into a square matrix, which includes matrix rotation, permutation of rows, circular bit shifting, and finally an affine linear transformation followed by an additional Base62-like encoding layer to further …

Tapping Into The Ocean’S Hidden Energy: Feasibility Of A 5 Mw Otec Installation In North Bali, Widodo Setiyo Pranowo, Yani Permanawati, Gisela Malya Asoka Anindita, Agung Kurniawan, Albertus Sulaiman, Johar Setiyadi, Safri Burhanuddin, Ivonne Milichristi Radjawane, Hansan Park, Endro Sigit Kurniawan

Karbala International Journal of Modern Science

Indonesia is an archipelagic country surrounded by water and faces energy challenges due to the low use of renewable energy. Among the potential renewable options, marine energy is a particularly suitable resource given the country’s geographical nature. Based on this discussion, seawater temperature can be used as an alternative ocean thermal en-ergy known as ocean thermal energy conversion (OTEC) by using the difference in sea surface and deep-sea water temperatures. Therefore, this study aimed to examine OTEC installations in North Bali waters using closed-cycle OTEC calculations for a 5 MW system. Following this objective, we examined the water conditions by …

Assessment And Evidence Practices In Cybersecurity Education: A Systematic Review (2015–2025), James K. Mayberry

Journal of Cybersecurity Education, Research and Practice

This study presents a PRISMA-based systematic review of 412 cybersecurity education intervention studies, coding assessment methods, evidence types, claimed outcomes, use of established assessment instruments, and artifact availability. Despite frequent claims of skill development and workforce preparation, 45.4% of studies reported no identifiable assessment. Knowledge tests appeared in 11.4% of studies, while performance assessments appeared in 10.2%. From 2015 to 2025, assessment practices remained dominated by post-only designs or no assessment, with no statistically detectable increase in pre/post-capable designs. Use of established assessment instruments was rare, with 94.2% of assessed studies using ad hoc measures or not identifying an established …

Building Ai-Native Innovation System To Drive Transformation And Innovation In Research Organization And Management Models, Hong Xuehai

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence (AI) is profoundly reshaping research paradigms. This study aims to analyze the intrinsic mechanisms through which AI empowers scientific research and its impact on the organizational management models of research. By summarizing what AI can and cannot do in empowering research, it reveals the current effectiveness and capability boundaries of AI in this domain. Based on the extraction of common core conditions for AI-empowered research and the deconstruction of typical cases of AI-enabled research organizational models, this study analyzes the differences between the organizational management model of AI-empowered research and traditional research organizational models. Furthermore, it proposes three …

Understanding And Foresight On Construction Of Foundation Model Industry Innovation Ecosystem, Liu Liu, Chunhui Jia, Yangfan Han, Qiqi Zhang, Jialin Li, Dayuan Li, Junpu Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Foundation models are a key vehicle driving artificial intelligence toward general intelligence, and their industrialization urgently requires support from a systematic and collaborative innovation ecosystem. This study focuses on the construction of the foundation model industry innovation ecosystem. It first reviews the frontier progress and identifies its essence as a complex innovation network featuring the three-dimensional synergy of technological, organizational, and industrial architectures, and then analyzes the architecture along the upstream, midstream, and downstream of the industrial chain: the upstream supports computing power and data, the midstream undertakes algorithmic innovation and platform services, and the downstream realizes multi-scenario value transformation. …

Insights And Implications Of Ai For Science Strategies Of Major Science And Technology Powers, Zhang Zhiqiang, Yawei Shao

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence (AI) is transitioning from a research aid to a scientific discovery agent. The new paradigm of “AI + Science” (AI for Science, AI4S) – the intelligent science paradigm (or the fifth paradigm of science) – characterized by the deep integration of artificial intelligence into the entire process of scientific discovery, is rapidly emerging and becoming a “new agent” for intelligent and autonomous execution of scientific discovery and technological invention as well as a key force in reshaping the human knowledge production system and the global landscape of technological competition. The intervention of AI in the field of knowledge …

Open Sharing And Collaborative Governance: Practices And Implications Of National Institutes Of Health’S Digital Transformation, Long Yuntao, Bingzhi Wang, Zheping Xu, Yang Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

With the rapid development of information technology, research projects and tasks of research institutes are increasing rapidly. Scientific and technological resources are the core and foundation of scientific research work, and scientific research institutes are faced with the needs of processing, management and coordination of a large number of scientific and technological resources. Digital transformation has become an inevitable choice for the high-quality development of scientific research institutes. The U.S. National Institutes of Health (NIH), as the world’s top scientific research institute, has continuously introduced a number of initiatives in the field of digital transformation, such as institutional reform policies, …

Ai-Powered Knowledge Engines As Research Infrastructure For Systematic Knowledge Discovery, Gary Welz

Publications and Research

This paper proposes knowledge engines as a framework for understanding how intelligent systems — both human and artificial — systematically discover, integrate, and generate knowledge. We argue that history’s greatest scientific minds functioned as knowledge engines, processing information through iterative cycles of ingestion, analysis, synthesis, and communication, guided by curiosity and willingness to challenge established beliefs.

We propose a taxonomy of nine integrated capabilities — ingestion, digestion, analysis, calculation, comparison, connection, association, analogy, and multimodal communication — that any serious knowledge engine must combine systematically. The argument is deliberately integrative: achieving ambitious research goals requires orchestrating all nine capabilities within …

"The First Web Novel At 30: The Collection And The Creative Process", Robert Arellano, Scott Rettberg

ELO (un)supervised 2026

Summer 2026 marks the 30th anniversary of Sunshine '69, recognized as the first novelistic hypertext fiction published on the web. While the full work remains accessible online—an "(un)supervised" preservation achievement in itself—the archive remains split between boxes and memory. This conversation between the work's creator and a major scholar in electronic literature documents both specific preservation challenges and systemic patterns in what the field chooses to preserve.

Topics include: figuring out web-born composition before established methodologies existed; the three decades of technical decisions that kept a 1996 work alive through format obsolescence and server migrations; and what gets lost …

Llm-Enabled Open-Source Systems In The Wild: An Empirical Study Of Vulnerabilities In Github Security Advisories, Fariha Tanjim Shifat, Hariswar Baburaj, Ce Zhou, Jaydeb Sarker, Mia Mohammad Imran

Computer Science Faculty Research & Creative Works

Large language models (LLMs) are increasingly embedded in open-source software (OSS) ecosystems, creating complex interactions among natural language prompts, probabilistic model outputs, and execution-capable components. However, it remains unclear whether traditional vulnerability disclosure frameworks adequately capture these model-mediated risks. To investigate this, we analyze 295 GitHub Security Advisories published between January 2025 and January 2026 that reference LLM-related components, and we manually annotate a sample of 100 advisories using the OWASP Top 10 for LLM Applications 2025.We find no evidence of new implementation-level weakness classes specific to LLM systems. Most advisories map to established CWEs, particularly injection and deserialization weaknesses. …

Together//Apart Explorations In Choreorobotic Performance Ontologies, Kate Sicchio, Patrick J. Martin

Computer Science Faculty Publications

This chapter presents a practice-as-research approach to developing an improvisational choreorobotic performance. Our performance process motivated the creation of new human–robot interaction and live choreography technologies. These technologies were tested during the performance and evaluated by the audience through feedback on their perceptions about the coexistence of humans and machines in a shared space. Examining these results through both autonomous robotics and performance studies ontologies, we formulated a new analytical process in which choreographic practice informs design and robots inform performance.

Improving Cancer Diagnosis And Patient Outcomes With Deep Learning Models, Mariana Arriz-Jorquiera

USF Tampa Graduate Theses and Dissertations

Cancer care depends on timely and reliable decisions, from detection and diagnosis to treatment planning and patient monitoring. These decisions are often made under uncertainty because medical images and healthcare data may be noisy, incomplete, or difficult to interpret. In breast cancer imaging, ultrasound is widely used because it is safe, accessible, and complementary to other imaging modalities. However, variations in image quality, acquisition conditions, and noise can obscure lesion boundaries and texture, affecting human interpretation and artificial intelligence reliability. This dissertation develops deep learning, image-analysis, and optimization methods to improve healthcare decisions under imperfect information. Its primary focus is …

Deciphering The Potential Of Monopterus Albus Protein Hydrolysate As A Therapeutic Candidate Against Cervical Cancer By Integrating Content Analysis, Network Pharmacology, And Molecular Docking, Okid Parama Astirin, Widya Mega Rahmawati, Elisa Herawati, Tetri Widiyani, Lili Pandan Sari, Sherly Octaviana

Karbala International Journal of Modern Science

Cervical cancer has a high incidence rate in women, making the development of cancer drugs increasingly urgent. Asian swamp eel protein hydrolysate shows great promise as an anticancer candidate. This study aimed to reveal the potential of Asian swamp eel protein hydrolysate as an anticancer candidate in the cervix. Asian swamp eel protein hydrolysate was produced by hydrolyzing flesh with the enzyme alcalase. Compounds and peptides were identified using LC-HRMS. These compounds and peptides were analyzed using network pharmacology, gene ontology, and molecular docking. The results identified 153 endogenous compounds, including fatty acids and peptides. The key proteins targeted by …

Compressed Cinema As A Study In Llm Latent Spaces, Mallen Clifton

ELO (un)supervised 2026

In his article “Spec Acts” (2021), Matthew Kirschenbaum analyzes the AI-generated novel 1 the Road to develop his titular concept of the spec act, “the future in its multitudes collapsing into an actionable present.” With the proliferation of texts produced by generative AI and subsequent critical analyses of them, one element in particular calls for further theorization: “the future in its multitudes,” or more directly, the latent space. This echoes arguments by critics such as Antonio Somaini, who offered his own “Theory of Latent Spaces” last year. However, where Somaini’s attention is towards visual culture, I turn mine to the …

"Todo: Fix The Mess Gemini Created": Towards Understanding Genai-Induced Self-Admitted Technical Debt, Abdullah Al Mujahid, Mia Mohammad Imran

Computer Science Faculty Research & Creative Works

As large language models (LLMs) such as ChatGPT, Copilot, Claude, and Gemini become integrated into software development workflows, developers increasingly leave traces of AI involvement in their code comments. Among these, some comments explicitly acknowledge both the use of generative AI and the presence of technical shortcomings. Analyzing 6,540 LLM-referencing code comments from public Python and JavaScript-based GitHub repositories (November 2022-July 2025), we identified 81 that also self-admit technical debt (SATD). Developers most often describe postponed testing, incomplete adaptation, and limited understanding of AI-generated code, suggesting that AI assistance affects both when and why technical debt emerges. We term GenAI-Induced …